Analysis dynamic characteristics brushless motor of the mechatronic system in conditions of parametric uncertaintyby computer simulation methods by N. A. Malev, O. V. Pogoditsky, O. V. Kozelkov, A. M. Dyuryagin

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A comprehensive transplanting of black-box adversarial attacks from multi-class to multi-label models by Zhijian Chen, Qi Zhou, Yujiang Liu, Wenjian Luo

“…In this paper, we study the transplantation methods of multi-class black-box attack algorithms to multi-label classification models and propose the multi-label versions for eight classic black-box attack algorithms, which include three score-based attacks and five decision-based (label-only) attacks, for the first time. …”
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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

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Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients by Xiaoyan Yin, Yongbin Cui, Tonghai Liu, Zhenjiang Li, Huiling Liu, Xingmin Ma, Xue Sha, Changsheng Ma, Dali Han, Yong Yin

“…Patients were randomly divided into training cohort and validation cohort with a ratio of 7:3. The radiomics features were selected by LASSO algorithm. …”
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Research on Civil Aviation Airport Site Selection Considering Group Consensus Level Under Large-Scale Uncertain Information by Rui Wang, Jing-Han Zeng, Jing-Yang Huang, Rui Kang, Jiang Yuan, Qing-Wei Zhong

“…The method comprises five key processes: (1) Evaluation process: Based on the constructed multicriteria evaluation system for airport site selection, the q-Rung Orthopair Fuzzy (q-ROF) information is employed to represent evaluations from large-scale decision makers, which effectively characterizes the uncertainty of information and broadens the evaluative scope. (2) Clustering process: A clustering procedure is designed for large-scale q-ROF evaluation data and weight information of criteria, identifying and removing outliers. (3) Consensus reaching process: Considering the characteristics of q-ROF evaluations and multiplicative preference relations, two adaptive consensus reaching algorithms are developed to enhance group consensus levels, thereby improving the rationality of decision-making results. (4) Weight determination process: Criteria and subcriteria weights are calculated using multiplicative preference weighting approach and a deviation maximization model, respectively, derived from aggregated group evaluations. (5) Ranking process: The q-ROF Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is applied, in conjunction with the induced q-ROF information integration paradigm, to comprehensively rank the alternative sites. …”
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Identification of multiomics and immune infiltration-associated biomarkers for early gastric cancer: a machine learning-based diagnostic model development study by Kewei Du, Wenfei Hu, Shan Gao, Jianxin Gan, Chongge You, Shangdi Zhang

“…Conclusions TAGLN2, HSP90AB1, SH3BGRL3 and CFL1 are potential diagnostic biomarkers for early-stage GC, with strong associations with immune cell infiltration. …”
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Enhancing malignant transformation predictions in oral potentially malignant disorders: A novel machine learning framework using real-world data by Jing Wen Li, Meng Jing Zhang, Ya Fang Zhou, John Adeoye, Jing Ya Jane Pu, Peter Thomson, Colman Patrick McGrath, Dian Zhang, Li Wu Zheng

“…Using data from 1,094 patients across three institutions (2004–2023), the researchers compared traditional statistical methods, including a Cox proportional hazards (Cox-PH) nomogram, with machine learning (ML) algorithms. …”
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Deep learning-assisted analysis of biomarker changes after increase of dosing from aflibercept 2 mg to 8 mg in therapy-resistant neovascular age-related macular degeneration by Siegfried Priglinger, Jakob Siedlecki, Benedikt Schworm, Franziska Eckardt, Michael Hafner, Ben Asani, Caspar Liesenhoff, Alexander Kufner, Johannes Benedikt Schiefelbein

“…Since 01/2024, aflibercept 8 mg represents an additional treatment option and contains a four times higher dosage than the already known aflibercept 2 mg.Methods To evaluate the real-world efficacy of aflibercept 8 mg in refractory nAMD patients, focusing on changes in key optical coherence tomography biomarkers over a follow-up period of the first four aflibercept 8 mg injections using a deep learning-based semantic segmentation algorithm. Inclusion criteria were: switch to aflibercept 8 mg after insufficient response to aflibercept 2 mg, marked by persistent retinal fluid or inability to extend treatment beyond 6 weeks; completion of at least 3 months (90 days) follow-up under treat-and-extend treatment regime; and no confounding conditions like intraocular infection, uveitis or other retinal diseases.Results 23 eyes of 21 patients with therapy-resistant nAMD were switched to aflibercept 8 mg. …”
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Surgical management of peripheral nerve symptoms following knee arthroplasty by Otis C. van Varsseveld, Floris V. Raasveld, Wen-Chih Liu, Justin McCarty, Caroline A. Hundepool, J. Michiel Zuidam, Ian L. Valerio, Kyle R. Eberlin

“…This study evaluates peripheral nerve surgery following KA and proposes a treatment algorithm. Methods Patients who underwent peripheral nerve surgery for neuropathic symptoms (neuropathic pain and/or motor dysfunction) following KA between 2012–2024 (≥ 3-month follow-up) were included. …”
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Innovative Business Models Towards Sustainable Energy Development: Assessing Benefits, Risks, and Optimal Approaches of Blockchain Exploitation in the Energy Transition by Aikaterini Papapostolou, Ioanna Andreoulaki, Filippos Anagnostopoulos, Sokratis Divolis, Harris Niavis, Sokratis Vavilis, Vangelis Marinakis

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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

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Spatial dynamics and competitive pricing in charging-as-a-service markets: The quest for cleaner urban development by Guanyu Zhou, Qianyu Dong, Chenyang Feng, Youwei Jia

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Automated design framework for excavation retaining structures: Extending IFC standards and integrating BIM with geotechnical simulation by Qiwei Wan, Yuyuan Zhu, Haibin Ding, Wentao Hu, Changjie Xu

“…Key contributions include: (1) the extension of the IFC standard to support excavation retaining structures with objects like IfcBracedPit and IfcPitWall, improving interoperability between geotechnical models and BIM systems; (2) the integration of heuristic algorithms for automated optimization of deformation control parameters, reducing manual intervention; and (3) the promotion of design methodology that bypasses two-dimensional modeling and directly generates three-dimensional models, enhancing efficiency and allowing engineers to focus on high-level decision-making. …”
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A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification by Shravan Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, R. Jashwanth

“…Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. …”
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Classification of finger movements through optimal EEG channel and feature selection by Murside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Matjaž Perc, Matjaž Perc, Matjaž Perc, Matjaž Perc, Yalcin Isler

“…Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.ResultsFor subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. It also had the lowest Brier scores at all time points, and decision curve analysis (DCA) demonstrated the best clinical decision benefits compared to other models.…”
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The future of AI in government services and global risks: insights from design fictions by Pedro Vitor Marques Nascimento, Paloma Beatriz Belchior de Siqueira, Nathalia Chrispim, Ramon Miranda Chaves, Carlos Eduardo Barbosa, Jano Moreira de Souza

“…The findings highlight three critical dilemmas: (1) AI’s dual role in enhancing efficiency while exacerbating algorithmic bias and surveillance concerns; (2) the potential displacement of human roles in public services, raising questions about accountability and transparency; and (3) the ethical trade-offs in AI-driven decision-making, particularly in law enforcement, healthcare, and education. …”
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Enhancing SAR-ATR Systems’ Resistance to S2M Attacks via FUA: Optimizing Surrogate Models for Adversarial Example Transferability by Xiaying Jin, Shuangju Zhou, Chenyu Wang, Mingxin Fu, Quan Pan, Yang Li

“…By introducing an S2M transferability estimation between the surrogate and target models, FUA progressively optimizes the surrogate model from three aspects: model parameters, data distribution, and model architecture. …”
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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding by Fulin Wei, Xueyuan Xu, Tianyuan Jia, Daoqiang Zhang, Xia Wu

“…Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. Besides that, we design an inter-subject MI decoding framework to verify the effectiveness of these two MSTL algorithms. …”
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